AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.
Tomography (Ann Arbor, Mich.)
Jan 3, 2025
OBJECTIVES: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully au...
Novel transfer learning based bone fracture detection using radiographic images.
BMC medical imaging
Jan 3, 2025
A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture ca...
Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups.
European radiology
Jan 2, 2025
OBJECTIVES: Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-netw...
PFSH-Net: Parallel frequency-spatial hybrid network for segmentation of kidney stones in pre-contrast computed tomography images of dogs.
Computers in biology and medicine
Jan 2, 2025
Kidney stone is a common urological disease in dogs and can lead to serious complications such as pyelonephritis and kidney failure. However, manual diagnosis involves a lot of burdens on radiologists and may cause human errors due to fatigue. Automa...
BCNet: Bronchus Classification via Structure Guided Representation Learning.
IEEE transactions on medical imaging
Jan 2, 2025
CT-based bronchial tree analysis is a key step for the diagnosis of lung and airway diseases. However, the topology of bronchial trees varies across individuals, which presents a challenge to the automatic bronchus classification. To solve this issue...
A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images With Multi-Relationship Graph Learning.
IEEE transactions on medical imaging
Jan 2, 2025
Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated wit...
Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling.
IEEE transactions on medical imaging
Jan 2, 2025
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, howev...
Implementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.
European radiology
Dec 31, 2024
OBJECTIVES: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by ra...
Knee osteoarthritis severity detection using deep inception transfer learning.
Computers in biology and medicine
Dec 31, 2024
Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL...
Validation of AI-driven measurements for hip morphology assessment.
European journal of radiology
Dec 31, 2024
RATIONALE AND OBJECTIVES: Accurate assessment of hip morphology is crucial for the diagnosis and management of hip pathologies. Traditional manual measurements are prone to mistakes and inter- and intra-reader variability. Artificial intelligence (AI...
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